Predicting Player Trajectories in Shot Situations in Soccer

Abstract

Player behaviors can have a significant impact on the outcome of individual events, as well as the game itself. The increased availability of high quality resolution spatio-temporal data has enabled analysis of player behavior and game strategy. In this paper, we present the implementation and evaluation of an imitation learning method using recurrent neural networks, which allows us to learn individual player behaviors and perform rollouts of player movements on previously unseen play sequences. The method is evaluated using a 2019 dataset from the top-tier soccer league in Sweden (Allsvenskan). Our evaluation provides insights how to best apply the method on movement traces in soccer, the relative accuracy of the method, and how well policies of one player role capture the relative behaviors of a different player role, for example.

Identifying Completed Pass Types and Improving Passing Lane Models

“…Rim passing lanes are a natural extension of this work. We could further improve the passing lane model to include more advanced methods of expected movement, such as predicting a player's movement with machine learning (i.e, ghosting) [6], physics-based approaches used in soccer [10], or considering handedness, reach, and stick length. When modeling the expected speed of a pass, a future iteration may consider personalized pass profiles by observing previous passes only by a specific player, their location, position, orientation (augmented from a visual dataset since this is not in the PPT data), or type of pass (i.e., Direct, 1-bank, or Rim).…”

Computer vision systems and deep learning for the recognition of athlete's movement: A review article

“…In addition, the ball can be tracked inside the field, i.e. whether it crossed the goal line or not [20][21][22][23][24][25].…”

A Comprehensive Review of Computer Vision in Sports: Open Issues, Future Trends and Research Directions

“…The systems which evaluate the player [77] or team performance [78] have the potential to understand the game's aspects, which are not obvious to the human eye. These systems are able to evaluate the activities of players successfully [79] such as the distance covered by players, shot detection [80,81], the number of sprints, player's position, and their movements [82,83], the player's relative position concerning other players, possession [84] of the soccer ball and motion/gesture recognition of the referee [85], predicting player trajectories for shot situations [86]. The generated data can be used to evaluate individual player performance, occlusion handling [21] by the detecting position of the player [87], action recognition [88], predicting and classifying the passes [89][90][91], key event extraction [92][93][94][95][96][97][98][99][100][101], tactical performance of the team [102][103][104][105][106], and analyzing the team's tactics based on the team formation [107][108][109], along with generating highlights [110][111][112][113].…”